Modeling the Acquisition of Phonological Interactions: Biases and Generalization

In this paper we computationally implement four different theories for representing opaque and transparent phonological interactions: Harmonic Serialism, Stratal OT, Two-Level Constraints, and Indexed Constraints. We then show that these theories make unique predictions on two tasks: (1) a learning-bias task, based on previous experimental work with humans and (2) a novel generalization task that no human data exists for. Our results in (1) show that serial models predict that transparent languages should be easier to acquire, while parallel models do not. Furthermore, the results for (2) show that all four of the theories we test make unique predictions for how humans should generalize to novel phonological interaction types.

Review of series-parallel models for calculating the thermal conductivity of soils

The paper presents mixed models collected from the literature for calculating the thermal conductivity of the soil. They are created on the basis of combining the serial and parallel model. The thermal conductivity of the soil is the basic thermal parameter of the soil. Knowledge of it is necessary, among other things, for the proper design of underground infrastructure. The combination of models will help you to choose the method of calculating the thermal conductivity of the soil that gives the most accurate results and has the lowest error.

Textual characteristics of multicomponent structures built according to sequential subordination and parallel models

The object of the analysis is complex sentences including two or more subordinate clauses with the meaning of condition, cause, purpose, concession, which in general constitute the category of conditionality. Multicomponent sentences expressing the close interconnection of these relations represent a microtext, making it possible to study various textual characteristics. The analysis is based on the multicomponent complex sentences built according to models of sequential subordination and parallel models, and is devoted to their textual characteristics. The latter include both universal text categories inherent in any text, and optional, inherent only in certain types of text. It is shown how the main text categories such as cohesion, processuality, integrity, continuum are realized within the microtext-sentence. The interrelation of the structure of multicomponent complex sentences, their content and the communicative task of the statement is emphasized. On the example of contaminated structures with various types of connection (compositional, subordinate, asyndetic), it is shown that the combination of multiple methods and techniques constitutes tectonic means of text formation. It is confirmed that in the structural-semantic context, the main nominative and communicative units of the language system, a word and a sentence, acquire meaning increments and turn into “text words” and “text sentences”. It is concluded that multicomponent complex sentences with subordinate clauses are microtext with more or less integral meaning. The combination of various types of syntactic link, various types of inclusions, repetition, parallelism, providing a clear structural form of complex constructions, on the one hand, are means of dispersing meanings, and on the other hand, they unite separate structural and semantic components of the statement into a single whole, creating the basis of text-forming relations.

A comparative study of three new parallel models based on the PSO algorithm

Meta-heuristic PSO has limits, such as premature convergence and high running time, especially for complex optimization problems. In this paper, a description of three parallel models based on the PSO algorithm is developed, on the basis of combining two concepts: parallelism and neighborhood, which are designed according to three different approaches in order to avoid the two disadvantages of the PSO algorithm. The third model, SPM (Spherical-neighborhood Parallel Model), is designed to improve the obtained results from the two parallel NPM (Neighborhood Parallel Model) and MPM (Multi-PSO Parallel Model) models. The experimental results presented in this paper show that SPM model performed much better than both NPM and MPM models in terms of computing time and solution quality.

Using Hadoop MapReduce for Parallel Genetic Algorithms: A Comparison of the Global, Grid and Island Models

The need to improve the scalability of Genetic Algorithms (GAs) has motivated the research on Parallel Genetic Algorithms (PGAs), and different technologies and approaches have been used. Hadoop MapReduce represents one of the most mature technologies to develop parallel algorithms. Based on the fact that parallel algorithms introduce communication overhead, the aim of the present work is to understand if, and possibly when, the parallel GAs solutions using Hadoop MapReduce show better performance than sequential versions in terms of execution time. Moreover, we are interested in understanding which PGA model can be most effective among the global, grid, and island models. We empirically assessed the performance of these three parallel models with respect to a sequential GA on a software engineering problem, evaluating the execution time and the achieved speedup. We also analysed the behaviour of the parallel models in relation to the overhead produced by the use of Hadoop MapReduce and the GAs' computational effort, which gives a more machine-independent measure of these algorithms. We exploited three problem instances to differentiate the computation load and three cluster configurations based on 2, 4, and 8 parallel nodes. Moreover, we estimated the costs of the execution of the experimentation on a potential cloud infrastructure, based on the pricing of the major commercial cloud providers. The empirical study revealed that the use of PGA based on the island model outperforms the other parallel models and the sequential GA for all the considered instances and clusters. Using 2, 4, and 8 nodes, the island model achieves an average speedup over the three datasets of 1.8, 3.4, and 7.0 times, respectively. Hadoop MapReduce has a set of different constraints that need to be considered during the design and the implementation of parallel algorithms. The overhead of data store (i.e., HDFS) accesses, communication, and latency requires solutions that reduce data store operations. For this reason, the island model is more suitable for PGAs than the global and grid model, also in terms of costs when executed on a commercial cloud provider.